Electrocardiogram (ECG) is a common medical investigation technique, which is widely used in all healthcare for diagnosis and monitoring of numerous conditions from heart attacks to electrolyte imbalances.
Long term ambulatory ECG monitoring is highly desired to detect, characterize and document cardiac arrhythmias in clinical practice. It can also detect periods when a user's heart is suffering from the effects of inadequate blood supply or myocardial ischaemia. Several customized digital ECG signal processors have been developed. However, many of these mainly focus on the heart beat rate (HBR) calculation which is basically based on the R-R interval information retrieved from QRS peak detections.
For clinical treatment, information on QRS peak detection is not sufficient for comprehensive diagnosis. Clinical professionals also require other important features related to P and T waves, as well as noise filtering and clean ECG reconstruction. Some conventional devices have realized more comprehensive functions. For example, a multiple functional ECG signal processing for wearable applications of long-term cardiac monitoring has been proposed by X. Liu et al, as published on IEEE Trans. Biomed. Eng., vol. 58, no. 2, pp. 380-389, January 2011, which can perform noise suppression and baseline drifting removal to generate clean ECG waveforms. However, due to the increasing complexity in the signal processing algorithms, the consumptions on power and silicon areas are comparatively high for hardware implementation.
Thus, there is a need in the art for a robust ECG processing system which is able to provide clean ECG signal output with enhanced energy and area efficiency and reduced signal processing power consumption.
Further, there is also a need in the art for the robust ECG processing system to be able to analyse comprehensive cardiac features, which include not only the QRS peak complex, but also P waves and T waves.